Objective:

This National study is aimed at identifying factors that explain the heterogeneity of health risks associated with air pollution exposure. We hypothesize that such factors include medical and social conditions, conditions that modify exposure, and differences in pollution composition that modify exposure toxicity. Moreover, we hypothesize that the relevant factors vary among different health outcomes. We are conducting national studies of short- and long-term exposures to individual pollutants, sources, and mixtures. We have established a cohort of 2.3 million Medicare enrollees residing in Massachusetts and surrounding states and are following its members prospectively for cause-specific hospital admissions and mortality for the period 2000-2014, and are also studying all live births in Eastern MA, geo-coded to exact address and followed for adverse birth outcomes.

Summary/Accomplishments (Outputs/Outcomes):

During these years we were able to address all the specific aims by conducting epidemiological studies and also by developing statistical methods. The published work addresses the specific aims of Project 5 and is in the areas of health effects of air pollution mixtures, health effects of extreme weather, climate change and adaptation, development of statistical methods for estimating the health effects of mixtures and causal inference methods to assess the public health impact of air quality regulations. Highlights of our findings are summarized below.

Spatio-Temporal Exposure Models

Successful development and validation of exposure models in Project 5, used here and in other Center Projects have included models for PM2.5, temperature, and Ozone:

In Kloog et al. (2012, 2013, 2014), we have further improved our spatio-temporal model to predict daily PM2.5 concentrations, using land use regression terms and satellite remote sensing. These models have been used to predict PM2.5 exposures at a finer spatial scale (1 x 1 km) and at daily level for all the US.

High resolution satellite remote sensing data has also been used to estimate daily air temperature across the Northeastern (Shi et al., 2015) and Southeastern United States (Shi et al., 2016). In Shi et al. (2016), we used satellite based surface temperature (Ts) measurements to estimate 15-year daily local air temperature in the southeastern USA, by extending and validating the previous hybrid-model approach to account for the unique geographical and climatological characteristics of the study area. Specifically, by incorporating satellite derived Ts, Ta measured at monitors, meteorological variables and land use terms, we employed a 3-stage statistical modeling approach to obtain daily air temperature predictions at 1 km x 1 km resolution across the southeastern USA for the years 2000–2014. This dataset captures the spatial and temporal variations in Ta in warm areas, even in a large geographical area where topography and weather patterns vary considerably.

Recently, Di et al. (2016) predicted PM2.5 exposures at the 1x1 km grid across the continental United States with high spatio-temporal resolution using neural networks. Di et al. (2016) also used a hybrid model to predict spatially and temporally resolved ozone exposures in the Continental United States.

In Bell et al. (2013, 2014a), we have reviewed recent literature to summarize the state of scientific evidence on effect modification of the short-term exposure to PM2.5 (2013) and ozone (2014a). In Bell (2013) in 108 papers we found strong, consistent evidence that the elderly experience higher risk of particular matter–associated hospitalization and death, weak evidence that women have higher risks of hospitalization and death, and suggestive evidence that those with lower education, income, or employment status have higher risk of death. In Bell (2014a) we performed a meta-analysis for overall associations by age and sex; assessed publication bias; and qualitatively assessed sensitivity to socioeconomic indicators, race/ethnicity, and air conditioning. We found a strong evidence for higher risk in elderly and limited/suggestive evidence for higher associations among women compared to men. We also identified strong evidence for higher associations with unemployment or lower occupational status and weak evidence of sensitivity for racial/ ethnic minorities and persons with low education, in poverty, or without central air conditioning.

In Correia et al. (2013), we have conducted a nationwide epidemiological study on changes in PM2.5 in recent years and increased life expectancy for 545 US counties with reduced PM2.5 concentrations. In Kloog et al., we have conducted epidemiological studies to estimate acute and chronic effects of air pollution in elderly (2012); the association between pregnancy, PM2.5 exposure, premature birth and birth weight (2012); and the long and short term exposure to PM2.5 and mortality (2013). In all these studies the PM2.5 exposure was estimated by our novel prediction models for exposure combining land use regression with monitors concentrations and physical measurements (satellite aerosol optical depth).

In Kloog et al., 2012 we investigated both the long and short term effects of PM2.5 exposures on hospital admissions for respiratory, cardiovascular disease, stroke and diabetes across New-England. We found that chronic exposure to particles is associated with substantially larger increases in hospital admissions than acute exposure and both can be detected simultaneously using our exposure models.

In another paper by Kloog et al. (2012) we evaluated the relationship between premature birth and birth weight with exposure to ambient particulate matter (PM2.5) levels during pregnancy in Massachusetts for a 9-year period (2000–2008). We concluded that exposure to PM2.5 during the last month of pregnancy contributes to risks for lower birth weight and preterm birth in infants.

In Kloog et al., 2013 we investigated the long- and short-term effects of PM2.5 exposures on population mortality in Massachusetts, United States, for the years 2000–2008. Our novel PM2.5 exposure model allows us to gain spatial resolution in acute effects and an assessment of long-term effects in the entire population rather than a selective sample from urban locations.

In Bell et al. (2014b), we have estimated the association of PM2.5 constituents and sources with hospital admissions in four counties in Connecticut and Massachusetts (USA) for persons ≥ 65 years of age. We found that some particle sources and constituents are more harmful than others and that in this region the most harmful particles include black carbon, calcium, and road dust PM2.5.

Bell et al. (2015) examined whether the effect of ambient PM2.5 on the risk of hospital admissions differ for men and women. We found that the point estimates of PM2.5 exposure related risk were higher for women than men for almost all causes of hospitalization, including respiratory tract infection, cardiovascular disease, and heart rhythm disturbance admissions. These differences remained after stratification by age or season.

In Zanobetti et al. (2014) we used Medicare data during the period 1999 to 2010 in 121 US cities to estimate the short term effects of PM2.5 on mortality separately for Medicare enrollees that have had a previous hospitalization for diabetes or a neurological disorder. We found that short-term exposure to fine particles increased the risk of hospitalizations for Parkinson’s disease and diabetes, and of all-cause mortality. We believe that these results provide useful insights regarding the mechanisms by which particles may affect the brain. A better understanding of the mechanisms will enable the development of new strategies to protect individuals at risk and to reduce detrimental effects of air pollution on the nervous system. Kioumourtzoglou et al. (2015) subsequently assessed the potential impact of long-term PM2.5 exposure on event time, defined as time to the first admission for dementia, Alzheimer’s or Parkinson’s diseases in an elderly population across the Northeastern US, and we found strong evidence of an association with the three outcomes.

In Kioumourtzoglou et al. (2013, 2014) we have estimated effects of primary organic particles on emergency hospital admissions among the elderly in 3 US cities and we have conducted a study in Boston to assess the impact of source contribution uncertainty on the effects of source-specific PM2.5 on hospital admissions.

In Kioumourtzoglou et al. (2013) we categorized 58 primary organic compounds by their chemical properties into 5 groups: n-alkanes, hopanes, cyclohexanes, PAHs and isoalkanes, and we examined their impacts on emergency hospital admissions among Medicare recipients in 3 US cities. Results suggest that week-long exposures to traffic-related, primary organic species are associated with increased rate of total and cause-specific CVD emergency hospital admissions. Associations were significant for cyclohexanes, but not hopanes, suggesting that chemical properties likely play an important role in primary OC toxicity.

Kioumourtzoglou et al. (2016) in 207 US Cities estimated the effects between long-term PM2.5 exposures and mortality and assessed whether community-level variables, including socioeconomic status indicators and temperature, modify the PM2.5-mortality association. We found significant increases in mortality for increase in annual PM2.5 concentration in the Southeastern, Southern, and Northwestern regions, a borderline significant increase in the Central US region, and null or negative associations in the Northeastern, North Central, Southwestern, and Western regions. We then observed significant increases in mortality associated with an increase in annual PM2.5 concentration at the 25th and 75th percentiles for racial demographics, median household income, percent of families below poverty level, education levels, and obesity rates.

In Dominici et al. (2014), we have published a thought provoking commentary in Science contrasting analyses of observational data versus a quasi-experimental design in air pollution epidemiology.

In the last 40 years, the evidence that has led to revisions of the U.S. National Ambient Air Quality Standards has come mainly from observational studies aimed at estimating an exposure-response relation. There is a growing consensus in economics, political science, statistics, and other fields that the associational or regression approach to inferring causal relations—on the basis of adjustment with observable confounders—is unreliable in many settings. We discuss how quasi-experimental (QE) techniques provide an opportunity to improve understanding of the relation between human health and regulation of air pollution from particulates.

Schwartz et al. (2015) used 3 causal modeling methods with different assumptions and strengths to address whether there was a causal association between PM2.5 exposure and daily deaths in Boston. Findings of this study suggest that the association is causal. Schwartz et al. (2016) also estimated the causal effects of local PM2.5 at low concentration on mortality, finding that causal association holds at concentrations that are below current U.S. air quality standards, indicating that important public health benefits can follow from further control efforts.

In another study Shi et al. (2016), we estimated the acute and chronic effects of PM2.5 on mortality in the Medicare population and focused on the effect at low concentration of PM2.5. While there was no evidence of deviations from linearity for short-term exposures, spline models of long-term exposures indicated larger effect estimates for mortality for exposures ≥ 6 μg/m3.

Interaction between pollution exposure and weather/climate change on health effects

Zanobetti et al. (2015) in an editorial discussed the recent literature on the interactions between pollution and weather. The authors discussed the need for development of studies optimizing the collection of appropriate data and advancing methodological approaches to better assess the complex interplay between weather and ambient aerosols. They concluded that the combined analysis of ambient air pollution and weather jointly poses more challenges than currently acknowledged by most attempts to tackle them.

Wang et al. (2016) estimated the causal effects of long-term PM2.5 exposure on mortality in New Jersey. For this analysis, we applied a variant of the difference-in-differences approach, which serves to approximate random assignment of exposure across the population and hence estimate a causal effect. We observed an increase in all natural-cause mortality for the whole population. Mean summer temperature and mean winter temperature in a census tract significantly modified the effect of long-term exposure to PM2.5 on mortality. Increases in mortality associated with increases in PM2.5 exposure were observed in census tracts with more blacks, lower home value, or lower median income. Under the assumptions of our difference-in-different approach (that yearly deviations from the state-wide yearly fluctuations in PM2.5 by tract are unlikely to be associated with changes in other risk factors), we have identified a causal effect of long-term PM2.5 on mortality, which is modified by seasonal temperatures and community level socio-economic status. The effect estimates of PM2.5 in this study were comparable to those observed in previous cohort studies.

In Shi et al. (2015), we used satellite remote sensing to estimate temperature at the zip code level in all of New England, and performed the first general population study to examine chronic effects of long term temperature changes on life expectancy. We reported that increased variability in temperature in the summer and winter were associated with increased death rates. In Kloog 2015, using the same temperature model, we reported that temperature was associated with changes in birth weight and gestational age in Massachusetts.

Wang et al. (2016) in another study estimated the effect of cold waves on mortality in 209 US Cities. In this study, we found that cold waves (both immediate and lingering) were associated with an increased, albeit small, risk of mortality, with associations varying substantially across different climatic conditions. We also observed that the risk increased with the duration and intensity of the cold wave and decreased with increasing mean winter temperature. Using projected cold waves using 20 downscaled climate models, we subsequently found that the projected mortality due to cold waves would decrease from 1960 to 2050, although any decrease is likely small.

Development of innovative statistical methods

In addition to conducting many epidemiological studies on particulate matter, its chemical composition and sources, the team has been very productive in developing new statistical methods. In Wang et al., we have developed a new approach that accounts for the uncertainty associated with the confounding adjustment in epidemiological studies of air pollution and health. This paper was favorably received by Dr. Thomas a member of the advisory board and we are now extending this work to nonlinear outcomes and to the case where we deal with multiple exposure variables.

In Bobb et al. (2013), we have developed a new class of Bayesian hierarchical models to estimate the joint effect associated with simultaneous exposure to more than one pollutant. We have applied this newly developed method to a multi-site time series study to estimate the joint effect of PM10 and ozone on mortality. Findings indicated a larger relative risk of cardiovascular admissions associated with levels of PM2.5 and O3 higher than their national standards in locations with high average NO2 compared to locations with low average NO2.

In Bobb et al. (2014), we have conducted a study to estimate the trend over time of the risk of mortality associated with short term exposure to temperature. We found evidence that the trend is declining, and that air conditioning use is not a factor that explains such a decline. In Cefalu et al. (2014) we have conducted a study to better understand the role of confounders when some of these confounders are also used to predict exposure to air pollution. Using theoretical arguments and simulation studies, we show that the bias of a health-effect estimate is influenced by the exposure prediction model, the type of confounding adjustment used in the health-effects regression model, and the relationship between these two. Moreover, we argue that even with a health-effects regression model that properly adjusts for confounding, the use of a predicted exposure can bias the health-effect estimate unless all confounders included in the health-effects regression model are also included in the exposure prediction model.

In Zigler et al. (2012, 2014) we argue that the regulatory environment surrounding air pollution control policies warrants a new type of epidemiological evidence. Whereas air pollution epidemiology has historically informed policies with estimates of exposure-response relationships between pollution and health outcomes, these estimates alone cannot support current debates surrounding the actual health impacts of air quality regulations. We have developed a new statistical approach aimed at assessing (using causal inference arguments), the effect of attainment versus not attainment status to the NAAQS for PM10, both on the levels of pollution (PM10 and O3) but also on the outcome (mortality). The statistical approach allows us to assess separately indirect and direct effects of air quality regulations on health outcomes. Zigler and Dominici (2014) propose a Bayesian method for variable selection and model averaged causal effects. In this paper, we proposed three Bayesian methods for Propensity Score (PS) variable selection and model averaging. We illustrated features of our approaches with a simulation study, and ultimately used our methods to compare the effectiveness of surgical vs. nonsurgical treatment for brain tumors among 2,606 Medicare beneficiaries.

In Chung et al. (2014), we have developed a Bayesian spatially varying coefficient model for estimating the health effects of long-term PM2.5 exposure and the effect modification by the chemical composition.

Conclusions:

The goal of this Project was to do a National study to identify factors that explain the heterogeneity of health risks associated with air pollution exposure. Through the extensive literature that we were able to publish during these years and using the largest available collection of national datasets, we were able to address the specific aims by estimating the adverse health risks associated with short- and long-term exposure to individual pollutants, source types and mixtures on national and regional scales, and to identify factors that explain the heterogeneity of the air pollution health risks, such as characterization of susceptibility factors, such as age, gender, race, and pre-existing health conditions, and other factors such as temperature and socio-economic status. The epidemiological studies were complemented by developing novel statistical methods to address the specific aims, to assess the public health impact of air quality regulations, causal inference methods, variable selection, and Bayesian modeling averages.

Correia AW, Pope III CA, Dockery DW, Wang Y, Ezzati M, Dominici F. Effect of air pollution control on life expectancy in the United States:an analysis of 545 U.S. counties for the period 2000 to 2007. Epidemiology 2013;24(1):23-31.

Zanobetti A, O'Neill MS, Gronlund CJ, Schwartz JD. Summer temperature variability and long-term survival among elderly people with chronic disease. Proceedings of the National Academy of Sciences of the United States of America 2012;109(17):6608-6613.

Main Center Abstract and Reports:

Subprojects under this Center:(EPA does not fund or establish subprojects; EPA awards and manages the overall grant for this center).R834798C001 Relative Toxicity of Air Pollution MixturesR834798C002 Cognitive Decline, Cardiovascular Changes, and Biological Aging in Response to Air PollutionR834798C003 Identifying the Cognitive and Vascular Effects of Air Pollution Sources and Mixtures in the Framingharn Offspring and Third Generation CohortsR834798C004 Longitudinal Effects of Multiple Pollutants on Child Growth, Blood Pressure and CognitionR834798C005 A National Study to Assess Susceptibility, Vulnerability, and Effect Modification of Air Pollution Health Risks

The perspectives, information and conclusions conveyed in research project abstracts, progress reports, final reports, journal abstracts and journal publications convey the viewpoints of the principal investigator and may not represent the views and policies of ORD and EPA. Conclusions drawn by the principal investigators have not been reviewed by the Agency.